Study analyzes FIT schemes under market and regulatory uncertainty.
problem Tackles uncertainty in feed-in tariffs and their impact on investment thresholds.
method Uses semi-analytical real options framework to model and compare FIT schemes.
result Increasing regulatory uncertainty lowers investment thresholds for FIT schemes.
Study shows thresholding scheme converges for mean curvature flow of convex sets.
problem Analyzing convergence of thresholding scheme for mean curvature flow.
method Time discretization using Merriman, Bence and Osher's scheme, focusing on two-phase mean convex settings.
result Time-integrated energy of approximation converges to limit's energy in minimizing movements interpretation.
The MBO scheme for data clustering is analyzed in the large data limit, proving convergence to optimal partition problems.
problem Analyzing the MBO scheme for data clustering in the large data limit.
method Implicit gradient descent on the thresholding energy of a similarity graph.
result The MBO scheme outcomes converge to minimizers of a weighted optimal partition problem.
Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to ill-posed inverse problems. In this letter, we present a data-driven scheme for learning optimal thresholding functions for ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers of a simpl…
Spiking neuronal networks are usually simulated with three main simulation schemes: the classical time-driven and event-driven schemes, and the more recent hybrid scheme. All three schemes evolve the state of a neuron through a series of checkpoints: equally spaced in the first scheme and determined neuron-wise by spik…
Improves graph recovery in Gaussian graphical modeling.
problem Calibrating regularization parameters for graph recovery.
method Thresholded adaptive validation applied to graphical lasso.
result Thresholding pipeline improves graph recovery.
Detects crypto pump-and-dump schemes with a thresholding-based model.
problem Detecting genuine anomalies from minor trading fluctuations.
method Combining threshold-based criteria with EWMA and volatility measures.
result Balances high true-positive detection with minimal noise.
STAT-SVD method reduces high-dimensional data sparsity, achieving optimal estimation.
problem Sparse tensor singular value decomposition for high-dimensional data.
method STAT-SVD method with double projection & thresholding scheme.
result STAT-SVD provides sharp thresholding criterion and minimax rate-optimal estimation.
Optimal method detects jumps in jump-diffusion processes.
problem Detecting jumps in jump-diffusion processes with improved finite-sample performance.
method Iterative threshold-kernel method to optimally select threshold parameter.
result Approximate optimal threshold depends on spot volatility, jump intensity, and jump density.
We present a hybrid continuum-atomistic scheme which combines molecular dynamics (MD) simulations with on-the-fly machine learning techniques for the accurate and efficient prediction of multiscale fluidic systems. By using a Gaussian process as a surrogate model for the computationally expensive MD simulations, we use…
New method uses overcomplete frames for better acoustic scene analysis.
problem Improving acoustic scene analysis in real-world applications.
method Risk minimization-based overcomplete frame thresholding.
result Validated on bird activity detection task using wavelets.
Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits the applicability of these methods in large-scale problems, or in…
This paper considers the optimal dividend payment problem in piecewise-deterministic compound Poisson risk models. The objective is to maximize the expected discounted dividend payout up to the time of ruin. We provide a comparative study in this general framework of both restricted and unrestricted payment schemes, wh…
This paper considers the problem of estimating multiple related Gaussian graphical models from a p-dimensional dataset consisting of different classes. Our work is based upon the formulation of this problem as group graphical lasso. This paper proposes a novel hybrid covariance thresholding algorithm that can effecti…
Study uses active learning to automate EEG event annotation.
problem Lack of annotated clinical EEG data for machine learning models.
method Active learning algorithm for automated annotation of six types of EEG events.
result Recognition performance improved 2% absolute, capable of auto-annotating.
This paper explores how random sampling and coding can speed up approximate matrix multiplication.
problem Efficiently computing large-scale matrix multiplications in distributed systems.
method Proposes two schemes: coding for recovery and random sampling for approximation.
result Investigates tradeoffs between recovery threshold and approximation error.
Optimizes threshold selection for variance estimation in financial models.
problem Estimating integrated variance in financial models with jumps.
method Optimizes threshold selection using mean and conditional mean square error criteria.
result Proposes a novel method to approximate the optimal threshold.
Adaptive neural network improves MIMO detection on real-world channels.
problem Challenges in symbol detection for Massive MIMO.
method MMNet, a deep learning MIMO detection scheme that uses iterative soft-thresholding and temporal/spectral correlation.
result Significantly outperforms existing approaches on realistic channels with lower computational complexity.
We consider the problem of clustering noisy high-dimensional data points into a union of low-dimensional subspaces and a set of outliers. The number of subspaces, their dimensions, and their orientations are unknown. A probabilistic performance analysis of the thresholding-based subspace clustering (TSC) algorithm intr…
This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection. In this letter, available results are extended to the vector case and the GLRT detector and the optimal quantizer design are obtained. Also, a …
HMQ improves quantization for edge devices with mixed precision.
problem Efficient quantization for edge devices with uniform, power-of-two thresholds.
method Introduces HMQ, a mixed precision quantization block that repurposes Gumbel-Softmax for searching over quantization schemes.
result Achieves competitive and state-of-the-art results on ImageNet despite restrictions.
Adaptive sampling framework for varying probabilities.
problem Choosing good sampling probabilities for tasks and streams.
method Derives a general framework for adaptively changing sampling probabilities via thresholds.
result Shows when adaptive thresholds can be treated as independent samples.
A new model for learning and unlearning predictors efficiently.
problem Learning and unlearning predictors from subsets of data.
method Proposes a ticketed model for learning-unlearning, allowing examples to present tickets for unlearning.
result Space-efficient schemes for various concept classes, including thresholds and parities.
The receiver operating characteristic (ROC) curve is a very useful tool for analyzing the diagnostic/classification power of instruments/classification schemes as long as a binary-scale gold standard is available. When the gold standard is continuous and there is no confirmative threshold, ROC curve becomes less useful…
To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a large spatial covariance matrix of the generalized m-dependent and β-mixing time series (with J variables and T…
Paper proposes a framework to manage trading uncertainty using signal thresholds.
problem Managing uncertainty in trading algorithms using signal thresholds.
method Using a theorem by Benveniste and Priouret, the paper deduces an Inventory Asymptotic Behaviour (IAB) Theorem to address trading uncertainty.
result The IAB Theorem provides the full distribution of inventory at any time for a well-formulated trading algorithm.
We develop mask iterative hard thresholding algorithms (mask IHT and mask DORE) for sparse image reconstruction of objects with known contour. The measurements follow a noisy underdetermined linear model common in the compressive sampling literature. Assuming that the contour of the object that we wish to reconstruct i…
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown. We propose a simple and low-complexity clustering algorithm based on thresholding the correlations between the data po…
Paper provides linear convergence guarantees for KZIHT and KZPT methods.
problem Solving linear equation systems with sparse constraints.
method Combines Kaczmarz and iterative thresholding methods, using reshuffling data sampling.
result KZIHT and KZPT converge linearly to sparse solutions.
New method models dewetting of anisotropic particles using numerical techniques.
problem Modeling dewetting dynamics of particles with varying surface energies.
method Level set numerical approach with convolution kernels to handle anisotropic interfacial energies.
result Validated numerical scheme supports merging and splitting of interfaces.
A new method monitors unstructured 3D shapes without registration.
problem Error-prone registration and mesh reconstruction steps in PCD monitoring.
method Intrinsic geometric properties of shapes, using Laplacian and geodesic distances.
result Effective monitoring of defects without registration and mesh reconstruction.
RELTA-SGLD stabilizes nonconvex SGLD updates with a lighter taming scheme.
problem Stabilizing superlinear stochastic-gradient updates in nonconvex optimization.
method Threshold-based taming with relative-growth principle for stability.
result Polynomial moment stability and first-order stationary accuracy in nonconvex SGLD.
Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie for the selection of groups of correlated variables. To investigate on the statistical properties of this scheme and in particular on its consistency properties, we set up …
High-performance quantum codes decoded with minimal data.
problem Efficient decoding of linear-rate LDPC quantum codes.
method Tessellations of hyperbolic manifolds, Coxeter groups, and Galois fields.
result Achieved encoding rate of 13/72 with high performance.
AdaPT-GMM improves multiple testing power with covariates.
problem Powerful and robust multiple testing with covariates.
method Covariate-assisted Gaussian mixture model with adaptive thresholding.
result AdaPT-GMM delivers high power in various scenarios.
Efficient distributed learning with Byzantine-resilient thresholding and error feedback.
problem Byzantine-resilient distributed learning with communication efficiency.
method Simple thresholding for Byzantine mitigation, compressed gradients and norms for aggregation, error feedback.
result Statistical error rate matches Yin et al.~\cite{dong} but with simpler schemes, and improved convergence with error feedback.
This study aimed to find temporal clusters for several commodity prices using the threshold non-linear autoregressive model. It is expected that the process of determining the commodity groups that are time-dependent will advance the current knowledge about the dynamics of co-moving and coherent prices, and can serve a…
The problem of clustering noisy and incompletely observed high-dimensional data points into a union of low-dimensional subspaces and a set of outliers is considered. The number of subspaces, their dimensions, and their orientations are assumed unknown. We propose a simple low-complexity subspace clustering algorithm, w…
MMD-B-Fair learns fair representations by minimizing MMD test power.
problem Learning fair representations of data while preserving target attributes.
method Kernel two-sample testing and block testing schemes.
result Minimizing MMD test power allows hiding sensitive attribute information.
A new algorithm of the analysis of correlation among economy time series is proposed. The algorithm is based on the power law classification scheme (PLCS) followed by the analysis of the network on the percolation threshold (NPT). The algorithm was applied to the analysis of correlations among GDP per capita time serie…
Deep learning uses ROC cost functions to improve virtual screening accuracy.
problem Challenges in training deep learning models for virtual screening, especially class imbalance and lack of ground truth labels.
method Proposes using ROC cost functions to optimize deep learning models for virtual screening, introduces new training schemes and cost functions.
result Demonstrates improved performance of ROC-based approaches on PubChem datasets.
Revisits fuzzy neural networks with generalized Hamming distance, simplifying BN and ReLU.
problem Improving neural network techniques using fuzzy logic and generalized Hamming distance.
method Introducing generalized Hamming distance to reinterpret BN and ReLU, proposing GHN.
result Batch normalization and ReLU can be simplified or removed without loss of performance.
SISR improves feature attribution in complex payoff schemes.
problem Distorted feature attributions due to non-additive payoff functions and high-dimensional feature spaces.
method Sparse Isotonic Shapley Regression (SISR) learns a monotonic transformation to restore additivity and enforces L0 sparsity.
result SISR achieves strong support recovery and stable attributions across various payoff schemes.
We present two graph-based algorithms for multiclass segmentation of high-dimensional data. The algorithms use a diffuse interface model based on the Ginzburg-Landau functional, related to total variation compressed sensing and image processing. A multiclass extension is introduced using the Gibbs simplex, with the fun…
Heavy Lasso improves robustness in high-dimensional linear regression with heavy-tailed errors.
problem Challenges of classical Lasso in handling heavy-tailed noise and outliers.
method Data-augmented soft-thresholding with Student's t-distribution loss.
result Heavy Lasso achieves comparable rates to Huber loss under theoretical bounds.
L-ARC improves model fairness by localizing risk guarantees.
problem Improving model fairness in tasks like image segmentation and wireless networks.
method Localized Adaptive Risk Control (L-ARC) updates a threshold function in RKHS to target localized statistical risk guarantees.
result L-ARC produces prediction sets with improved fairness across different data subpopulations.
Optimal iterative thresholding algorithms improve upon hard and soft thresholding.
problem Optimizing sparsity or rank constraints in optimization problems.
method Developed the notion of relative concavity for thresholding operators, finding a new class of operators that are optimal.
result A new class of thresholding operators, including ℓq thresholding and reciprocal thresholding, achieves the strongest convergence guarantee. Spectrally-truncated KRR outperforms full KRR for large data.
problem Computational intensity of KRR for large datasets.
method Spectrally truncating the kernel matrix to its largest r eigenvalues. result Spectrally-truncated KRR can outperform full KRR for all finite samples above a threshold.